SoroushVahidi/scidocs huggingface dataset:
A novel approach for efficient supergraph query processing on graph databases
(2009)

In recent years, large amount of data modeled by graphs, namely graph data, have been collected in various domains. Efficiently processing queries on graph databases has attracted a lot of research attentions. Supergraph query is a kind of new and important queries in practice. A supergraph query, q, on a graph database D is to retrieve all graphs in D such that q is a supergraph of them. Because the number of graphs in databases is large and subgraph isomorphism testing is NP-complete, efficiently processing such queries is a big challenge. This paper first proposes an optimal compact method for organizing graph databases. Common subgraphs of the graphs in a database are stored only once in the compact organization of the database, in order to reduce the overall cost of subgraph isomorphism testings from stored graphs to queries during query processing. Then, an exact algorithm and an approximate algorithm for generating significant feature set with optimal order are proposed to construct indices on graph databases. The optimal order on the feature set is to reduce the number of subgraph isomorphism testings during query processing. Based on the compact organization of graph databases, a novel algorithm of testing subgraph isomorphisms from multiple graphs to one graph is presented. Finally, based on all these techniques, a query processing method is proposed. Analytical and experimental results show that the proposed algorithms outper-form the existing similar algorithms by one to two orders of magnitude.
Eye images increase generosity , but not for long : the limited effect of a false cue
(2013)

⁎ Corresponding author. E-mail address: asparks@uoguelph.ca (A. Sparks). 1090-5138/$ – see front matter © 2013 Elsevier Inc. Al http://dx.doi.org/10.1016/j.evolhumbehav.2013.05.001 Article history: Initial receipt 5 November 2012 Final revision received 3 May 2013
A New Approach of Digital Forensic Model for Digital Forensic Investigation
(2011)

The research introduces a structured and consistent approach for digital forensic investigation. Digital forensic science provides tools, techniques and scientifically proven methods that can be used to acquire and analyze digital evidence. The digital forensic investigation must be retrieved to obtain the evidence that will be accepted in the court. This research focuses on a structured and consistent approach to digital forensic investigation. This research aims at identifying activities that facilitate and improves digital forensic investigation process. Existing digital forensic framework will be reviewed and then the analysis will be compiled. The result from the evaluation will produce a new model to improve the whole investigation process.
Real-time object tracking via CamShift-based robust framework
(2012)

In recent years, lots of object tracking methods have been presented for better tracking accuracies. However, few of them can be applied to the real-time applications due to high computational cost. Aiming at achieving better realtime tracking performance, we propose an adaptive robust framework for object tracking based on the CamShift approach, which is notable for its simplicity and high processing efficiency. An adaptive local search method is presented to search for the best object candidate to avoid that the CamShift tracker gets confused by the surrounding background and erroneously incorporates it into the object region. A Kalman filter is also incorporated into our framework for prediction of the object's movement, so as to reduce the search effort and possible tracking failure caused by fast object motion. The experimental results demonstrate that the proposed tracking framework is robust and computationally effective.
2.4 GHz inkjet-printed RF energy harvester on bulk cardboard substrate
(2015)

An experimental investigation on the inkjet-printed power harvester for 2.4GHz and review of RF characterization of substrate and printed conductors are presented in this paper. A one stage discrete rectifier based on a voltage doubler structure and a planar monopole antenna are fabricated on cardboard using inkjet printing. The performance of the whole system is examined by measuring the output voltage of the RF power harvester. By the utilization of the proposed idea, the fabrication of low-cost environmentally-friendly battery-less wireless modules is conceivable.
DCnet: A new data center network architecture
(2017)

The migration of computation and storage to cloud data centers has imposed new communication requirements and a need for fundamental changes in infrastructure. The cloud paradigm means that significant network communication will occur between servers in data centers. Furthermore, the migration of virtual machines and containers across physical servers means that in addition to traditional requirements of efficiency, scalability, and easy manageability, future data center networks must also support efficient mobility. To solve the problem, the paper proposes a radical redesign for data center networks, using a new architectural approach that we call DCnet. The DCnet approach changes the addressing and routing schemes at layers 2 and 3 completely. The new addressing mechanism allows an organization to assign addresses that span multiple data centers and permits VM mobility across data centers. Despite radical changes in addressing and routing, the DCnet architecture retains basics from Ethernet and IP, allowing existing hardware building blocks to be reused. The paper uses IPv6 in examples, but the new addressing model and address mobility approach can be applied to IPv4. By assigning each addressable entity a unique ID, and using the ID as the host suffix in an IP address, DCnet allows legacy operating systems and applications to be used without changes. The paper reports a simulation study that uses mininet and SDN to demonstrate the feasibility of the DCnet approach.
Software engineering for security: a roadmap
(2000)

doesn't need to be secure? Almost every softwarecontrolled system faces threats from potential adversaries, from Internet-aware client applications running on PCs, to complex telecommunications and power systems accessible over the Internet, to commodity software with copy protection mechanisms. Software engineers must be cognizant of these threats and engineer systems with credible defenses, while still delivering value to customers. In this paper, we present our perspectives on the research issues that arise in the interactions between software engineering and security.
A novel soil measuring wireless sensor network
(2010)

Emerging technologies have made low-power and low-cost wireless sensor networks feasible. This paper presents a hierarchical wireless sensor network for measuring soil parameters such as temperature and humidity. Specifically, we designed sensor nodes that are placed completely underground and are used to collect soil measurements. These nodes use their radios to deliver the collected measurements to one of multiple relay nodes located above ground. In turn, relay nodes that are capable of long-range communications forward the data collected from the network's sensor nodes to a base node, which is connected to a workstation. The proposed hierarchical wireless sensor network uses a probabilistic communication protocol to achieve a very low duty cycle and hence a long life-time for soil monitoring applications.
Improved FIFO Scheduling Algorithm Based on Fuzzy Clustering in Cloud Computing
(2017)

In cloud computing, some large tasks may occupy too many resources and some small tasks may wait for a long time based on First-In-First-Out (FIFO) scheduling algorithm. To reduce tasks’ waiting time, we propose a task scheduling algorithm based on fuzzy clustering algorithms. We construct a task model, resource model, and analyze tasks’ preference, then classify resources with fuzzy clustering algorithms. Based on the parameters of cloud tasks, the algorithm will calculate resource expectation and assign tasks to different resource clusters, so the complexity of resource selection will be decreased. As a result, the algorithm will reduce tasks’ waiting time and improve the resource utilization. The experiment results show that the proposed algorithm shortens the execution time of tasks and increases the resource utilization.
A Fast Approach for Semantic Similar Short Texts Retrieval
(2016)

Retrieving semantic similar short texts is a crucial issue to many applications, e.g., web search, ads matching, questionanswer system, and so forth. Most of the traditional methods concentrate on how to improve the precision of the similarity measurement, while current real applications need to efficiently explore the top similar short texts semantically related to the query one. We address the efficiency issue in this paper by investigating the similarity strategies and incorporating them into the FAST framework (efficient FrAmework for semantic similar Short Texts retrieval). We conduct comprehensive performance evaluation on real-life data which shows that our proposed method outperforms the state-ofthe-art techniques.
OpenStreetMap: User-Generated Street Maps
(2008)

The OpenStreetMap project is a knowledge collective that provides user-generated street maps. OSM follows the peer production model that created Wikipedia; its aim is to create a set of map data that's free to use, editable, and licensed under new copyright schemes. A considerable number of contributors edit the world map collaboratively using the OSM technical infrastructure, and a core group, estimated at approximately 40 volunteers, dedicate their time to creating and improving OSM's infrastructure, including maintaining the server, writing the core software that handles the transactions with the server, and creating cartographical outputs. There's also a growing community of software developers who develop software tools to make OSM data available for further use across different application domains, software platforms, and hardware devices. The OSM project's hub is the main OSM Web site.
Neural Networks: A Comprehensive Foundation
(1994)

El profesor Haykin ha escrito un libro muy interesante, que ya lleva su segunda edición, lo cual es extrafio dentro de la gran cantidad de libros que se producen en la actualidad. Es fácil encontrar en la literatura contemporánea la constante referencia que se hace a este libro; sin embargo, el libro no es lo que el título indica, ya que no es un libro ortodoxo de las teorías y técnicas que son conocidas como redes neuronales (también conocidas como sistemas conexionistas o sistemas neurocomputacionales), tampoco es un libro que presente los fundamentos o bases teóricas de las redes neuronales. neuronales, así como los grandes modelos de esta área de investigación, como son los perceptrones y las redes RBFN (Radial-Basis Function Networks). La presentación de estos cuatro capítulos es excelente.
Hypertrophy of labia minora: experience with 163 reductions.
(2000)

OBJECTIVE
Our purpose was to describe the surgical procedure, its results, and its complications and to determine whether patients are satisfied with surgical reduction of labia minora in cases of hypertrophy.


STUDY DESIGN
The records of 163 patients who underwent reduction of the labia minora during a 9-year period were reviewed. The ages of the patients ranged from 12 to 67 years (median, 26). Motives for requesting surgery were aesthetic concerns in 87% of the cases, discomfort in clothing in 64%, discomfort with exercise in 26%, and entry dyspareunia in 43%. Anatomic results were assessed 1 month postoperatively. Patient satisfaction was assessed by means of a mailed questionnaire.


RESULTS
No surgery-related significant complications were noticed. Anatomic results were satisfactory for 151 patients (93%). Ninety-eight completed questionnaires were returned. Eighty-one patients (83%) found that the results after surgery were satisfactory. Eighty-seven (89%) were satisfied with the aesthetic result, and 91 (93%) approved the functional outcome. Four patients (4%) would not undergo the same procedure again.


CONCLUSION
Labia minora reduction is a simple surgical procedure associated with a high degree of patient satisfaction.
Cue-based feeding in the NICU: using the infant's communication as a guide.
(2013)

Although studies have shown cue-based feeding can lead to earlier achievement of full oral feeding, the successful implementation of cue-based feeding has been constrained by the volume-driven culture, which has existed for many years in the NIC U. This culture was built on the notion that a "better" nurse is one who could "get more in," and infants who are "poor feeders" are ones who "can't take enough." The infant who feeds faster is often viewed as more skilled in this task-oriented approach. The feeding relationship and the infant's communication about the experience of feeding may not be nurtured. This article will explain the central role of the preterm infant's communication in successful cue-based feeding. When the infant is perceived as having meaningful behavior (i.e., communicative intent), the focus changes from a volume-driven to a co-regulated approach, through which the infant guides the caregiver. This is cue-based feeding.
Connective tissue graft to correct peri-implant soft tissue margin: A clinical report.
(2004)

This clinical report describes the use of a subepithelial connective tissue graft to recontour a soft tissue margin discrepancy for a single-implant crown in the anterior maxilla. This procedure demonstrates that the use of soft tissue grafts to correct an esthetic deficiency may be a feasible approach to establish new and stable peri-implant soft tissue contours. The patient presented was followed for 18 months.
Flexible Electronics and Displays: High-Resolution, Roll-to-Roll, Projection Lithography and Photoablation Processing Technologies for High-Throughput Production
(2005)

Flexible electronics are increasingly being used in a number of applications which benefit from their low profile, light weight, and favorable dielectric properties. However, despite these advantages, the range of practical, high-volume, applications for flexible electronics will remain limited in the future unless a number of challenges related to lithographic patterning on flexible substrates are successfully addressed. The most critical of these pertain to system parameters that affect the cost and performance of flexible circuits, including the resolution, panel size, process throughput, substrate distortion, material handling, and yield. We present a new class of roll-to-roll lithography systems, developed in recent years, that were designed to address the challenges in each of these critical areas. These systems provides high-resolution projection imaging over very large exposure areas, on flexible substrate materials. Additionally, they achieve high-precision alignment by means of image scaling to compensate for substrate distortion due to processing; and they also performs high-throughput photoablation, patterning millions of pixels simultaneously, by means of projection imaging. This technology is attractive for current and emerging applications, such as flexible circuit boards and flexible chip carriers, as well as for potential future applications such as such as flexible displays and macroelectronic systems.
Concept Detection on Medical Images using Deep Residual Learning Network
(2017)

Medical images are often used in clinical diagnosis. However, interpreting the insights gained from them is often a time-consuming task even for experts. For this reason, there is a need for methods that can automatically approximate the mapping from medical images to condensed textual descriptions. For identifying the presence of relevant biomedical concepts in medical images for the ImageCLEF 2017 Caption concept detection subtask we propose the use of a pretrained residual deep neural network. Specifically, a 50-layered resNet was used and retrained on the medical images. The proposed method achieved F1 Score 0.1583 on the test data.
Fair and balanced?: bias in bug-fix datasets
(2009)

Software engineering researchers have long been interested in where and why bugs occur in code, and in predicting where they might turn up next. Historical bug-occurence data has been key to this research. Bug tracking systems, and code version histories, record when, how and by whom bugs were fixed; from these sources, datasets that relate file changes to bug fixes can be extracted. These historical datasets can be used to test hypotheses concerning processes of bug introduction, and also to build statistical bug prediction models. Unfortunately, processes and humans are imperfect, and only a fraction of bug fixes are actually labelled in source code version histories, and thus become available for study in the extracted datasets. The question naturally arises, are the bug fixes recorded in these historical datasets a fair representation of the full population of bug fixes? In this paper, we investigate historical data from several software projects, and find strong evidence of systematic bias. We then investigate the potential effects of "unfair, imbalanced" datasets on the performance of prediction techniques. We draw the lesson that bias is a critical problem that threatens both the effectiveness of processes that rely on biased datasets to build prediction models and the generalizability of hypotheses tested on biased data.
Angels and Devils of Digital Social Norm Enforcement: A Theory about Aggressive versus Civilized Online Comments
(2017)

We develop a theory that explains when commenters choose to be aggressive versus civilized in social media depending on their personal social norm context. In particular, we enrich traditional social norm theory by introducing the concept of moral legitimacy. This concept suggests that justifications, particularly those that put social norm violators outside of moral boundaries, cause aggression. Using the diversity of 45,982 comments of a real-world online firestorm, our results confirm that social norm contexts matter strongly for online behavior. The developed theory challenges existing speculations about online aggression and helps to develop strategies to encourage enlightened, civilized discourse on the Internet.
A Sensing Chair Using Pressure Distribution Sensors
(2001)

One challenge in multimodal interface research is the lack of robust subsystems that support multimodal interactions. By focusing on a chair—an object that is involved in virtually all human–computer interactions, the sensing chair project enables an ordinary office chair to become aware of its occupant’s actions and needs. Surface-mounted pressure distribution sensors are placed over the seatpan and backrest of the chair for real time capturing of contact information between the chair and its occupant. Given the similarity between a pressure distribution map and a grayscale image, pattern recognition techniques commonly used in computer and robot vision, such as principal components analysis, have been successfully applied to solving the problem of sitting posture classification. The current static posture classification system operates in real time with an overall classification accuracy of 96% and 79% for familiar (people it had felt before) and unfamiliar users, respectively. Future work is aimed at a dynamic posture tracking system that continuously tracks not only steady-state (static) but transitional (dynamic) sitting postures. Results reported here form important stepping stones toward an intelligent chair that can find applications in many areas including multimodal interfaces, intelligent environment, and safety of automobile operations.
A Geometric Framework for Rectangular Shape Detection
(2014)

Rectangular shape detection has a wide range of applications, such as license plate detection, vehicle detection, and building detection. In this paper, we propose a geometric framework for rectangular shape detection based on the channel-scale space of RGB images. The framework consists of algorithms developed to address three issues of a candidate shape (i.e., a connected component of edge points), including: 1) outliers; 2) open shape; and 3) fragmentation. Furthermore, we propose an interestness measure for rectangular shapes by integrating imbalanced points (one type of interest points). Our experimental study shows the promise of the proposed framework.
DESC: enabling secure data exchange based on smart contracts
(2017)

Dear editor, As data are one of the most important factors of today’s intelligent systems but are relatively scarce, people try exchanging data with other organizations or public marketplaces. However, data exchange is now lack of a secure execution mechanism to automatically and fairly protect the rights which are seriously concerned by data owners. For instance, data owners cannot limit the identity of a data transferee who is banned to take the data by any way. Moreover, data transferees are also afraid that their applications for exchange may be unfairly treated. For instance, data owners could dynamically change data price when multiple transferees submit their applications at the same time. A data marketplace can take over the role of a trusted third party during data exchange, but single point failure is always a serious problem in a large-scale distributed system. Furthermore, these marketplaces usually use contracts based on natural languages which may be maliciously misinterpreted and cannot be automatically executed. Numerous researches have been presented to provide digital rights management (DRM) [1]. For instance, Zhu et al. [2] presented a privacypreserving video subscription scheme with the limitation of expire date. However, rights of data owners are more complex than rights of digital content owners. Decentralized and trustworthy enforcement for access control policies is a long-term issue. Han et al. [3] proposed an optimized mechanism which is a trusted and decentralized access control framework for the client/server architecture. These studies still need third party authorities which are used to execute or supervise the rights control, leading to a few trust and security issues. Existing studies [4] on attribute-based access control have been applied to many fields, such as e-commerce and Internet of Things. This article introduces attribute-based access control and proposes a secure framework for data exchange based on smart contracts (DESC) to enable automatic and fair access control. Featuring with decentralized, fair, transparent, immutable and traceable advantages, blockchain 2.0 [5] provides a generalized framework for implementing decentralized compute resources named smart contracts which define rights and policies in mathematical and programming forms. Once the predefined smart contract is triggered by a transaction, it can automatically execute the specific contractual clauses. To find out which data rights are seriously concerned by data owners, we investigate the DRM mechanisms and several data exchange platforms: GBDEX, national engineering lab for big data distribution and exchange technologies. Then,